Regulating AI at work: labour relations, automation, and algorithmic management
Notice bibliographique
Résumé
Recent innovations in artificial intelligence (AI) have been at the core of massive technological changes that are transforming work. AI is now widely used to automate business processes and replace labour-intensive tasks while changing the skill demands for those that remain. AI-based tools are also deployed to invasively monitor worker conduct and to automate HR management processes.\nThrough the dual lens of comparative labour law and employment relations research, the articles in this special issue of Transfer investigate the role of collective bargaining and government policy in shaping strategies to deploy new digital and AI-based technologies at work. Together, they give new insight into the conditions for encouraging broadly shared benefits from technological innovation while mitigating harm to workers and society.\nThe first section of this issue includes articles comparing union and policy responses to AI at the national level. De Stefano and Taes draw on research in eight EU countries to examine the risks of AI and automated decision-making systems, and union and regulatory strategies to address these risks. Collins and Atkinson discuss the intersection between legal frameworks, collective bargaining, and employers’ algorithmic management choices in ‘post-Brexit Britain’. Krzywdzinski et al. analyse how these issues play out in the more strongly regulated German system, discussing not only the risks to workers, but also how worker voice helps address the challenges management faces in implementing AI at work. Hassel and Özkiziltan also focus on Germany, but they present a more differentiated analysis of how effective responses may differ depending on the type of risk AI poses for work. Molina et al. conclude this section with a broader comparative analysis of policy and union responses to AI and algorithms in Denmark, Germany, Hungary, and Spain.\nThe articles in the second section are based on comparative case studies, allowing the authors to examine how and why worker representatives’ strategies and bargaining power differ across countries. Doellgast et al. compare union and works council responses to algorithmic management in two telecommunications companies in Germany and Norway. Pulignano et al. examine union strategies toward the linked digital and green transitions in the German and Belgian auto industries. Finally, Garneau et al. compare union responses to digitalisation in aerospace manufacturing in Wallonia, Denmark, and Quebec. These three articles show that unions and works councils had most influence over technology-related decisions when they could draw on formal bargaining rights and encompassing collective agreements; as well as a network of intermediary institutions that support knowledge and strategy development and exchange.\nTogether, the articles make a strong case that efforts to better regulate the use of AI and algorithms at work are likely to be most effective where they are underpinned by, and supportive of, social dialogue. Individual legal protections are blunt instruments without mechanisms that also strengthen worker voice in, and oversight over, how technologies are implemented. Collective labour rights are the most effective tools to give workers real voice in the distribution of benefits or costs from the AI- and data-driven ‘digital revolution’. The articles also suggest specific lessons for unions and policy-makers seeking to develop broader strategies to engage with AI and digitalisation at work. We hope that they contribute to these crucial endeavours, by providing both an analytical base and comparative examples to support these strategies.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».